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  • 1.
    Laptev, Ivan
    et al.
    IRISA/INRIA.
    Caputo, Barbara
    Schüldt, Christian
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Local velocity-adapted motion events for spatio-temporal recognition2007In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 108, no 3, p. 207-229Article in journal (Refereed)
    Abstract [en]

    In this paper, we address the problem of motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate the problem of motion recognition as a matching of corresponding events in image sequences. To enable the matching, we present and evaluate a set of motion descriptors that exploit the spatial and the temporal coherence of motion measurements between corresponding events in image sequences. As the motion measurements may depend on the relative motion of the camera, we also present a mechanism for local velocity adaptation of events and evaluate its influence when recognizing image sequences subjected to different camera motions. When recognizing motion patterns, we compare the performance of a nearest neighbor (NN) classifier with the performance of a support vector machine (SVM). We also compare event-based motion representations to motion representations in terms of global histograms. A systematic experimental evaluation on a large video database with human actions demonstrates that (i) local spatio-temporal image descriptors can be defined to carry important information of space-time events for subsequent recognition, and that (ii) local velocity adaptation is an important mechanism in situations when the relative motion between the camera and the interesting events in the scene is unknown. The particular advantage of event-based representations and velocity adaptation is further emphasized when recognizing human actions in unconstrained scenes with complex and non-stationary backgrounds.

  • 2.
    Nilsson, John-Olof
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Schüldt, Christian
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Limes Audio, Sweden .
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Voice radio communication, pedestrian localization, and the tactical use of 3D audio2013In: 2013 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013, IEEE Computer Society, 2013, p. 6817918-Conference paper (Refereed)
    Abstract [en]

    The relation between voice radio communication and pedestrian localization is studied. 3D audio is identified as a linking technology which brings strong mutual benefits. Voice communication rendered with 3D audio provides a potential low secondary task interference user interface to the localization information. Vice versa, location information in the 3D audio provides spatial cues in the voice communication, improving speech intelligibility. An experimental setup with voice radio communication, cooperative pedestrian localization, and 3D audio is presented and we discuss high level tactical possibilities that the 3D audio brings. Finally, results of an initial experiment, demonstrating the effectiveness of the setup, are presented.

  • 3.
    Schüldt, Christian
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Blind low-complexity estimation of reverberation time2013In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE conference proceedings, 2013, p. 6701875-Conference paper (Refereed)
    Abstract [en]

    Real-time blind reverberation time estimation is of interest in speech enhancement techniques such as e.g. dereverberation and microphone beamforming. Advances in this field have been made where the diffusive reverberation tail is modeled and the decay rate is estimated using a maximum-likelihood approach. Various methods for reducing the computational complexity have also been presented. This paper proposes a method for even further computational complexity reduction, by more than 60% in some cases, and it is shown through simulations that the results of the proposed method are very similar to that of the original.

  • 4.
    Schüldt, Christian
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Decay Rate Estimators and Their Performance for Blind Reverberation Time Estimation2014In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, ISSN 2329-9290, Vol. 22, no 8, p. 1274-1284Article in journal (Refereed)
    Abstract [en]

    Several approaches for blind estimation of reverberation time have been presented in the literature and decay rate estimation is an integral part of many, if not all, of such approaches. This paper provides both an analytical and experimental comparison, in terms of the bias and variance of three common decay rate estimators; a straight-forward linear regression approach as well as two maximum-likelihood based methods. Situations with and without interfering additive noise are considered. It is shown that the linear regression based approach is unbiased if no smoothing is applied, and that the estimation variance in the absence of noise is constantly about twice that of the maximum-likelihood based methods. It is shown that the methods that do not take possible noise into account suffer from similar estimation bias in the presence of noise. Further, a hybrid method, combining the noise robustness and low computational complexity advantages of the two different maximum-likelihood based methods, is presented.

  • 5.
    Schüldt, Christian
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Noise robust integration for blind and non-blind reverberation time estimation2015In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, IEEE Signal Processing Society, 2015, p. 56-60Conference paper (Refereed)
    Abstract [en]

    The estimation of the decay rate of a signal section is an integral component of both blind and non-blind reverberation time estimation methods. Several decay rate estimators have previously been proposed, based on, e.g., linear regression and maximum-likelihood estimation. Unfortunately, most approaches are sensitive to background noise, and/or are fairly demanding in terms of computational complexity. This paper presents a low complexity decay rate estimator, robust to stationary noise, for reverberation time estimation. Simulations using artificial signals, and experiments with speech in ventilation noise, demonstrate the performance and noise robustness of the proposed method.

  • 6.
    Schüldt, Christian
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    On Implications of the ISO 3382 Backward Integration Method for Automated Decay Rate Estimation2015In: Journal of The Audio Engineering Society, ISSN 0004-7554, Vol. 63, no 3, p. 161-173Article in journal (Refereed)
    Abstract [en]

    The Schröder backward integration method for estimating the reverberation time of an enclosure, as suggested in the ISO 3382 standard, is analyzed from an estimation theoretic perspective, in a general context that is applicable to both blind and non-blind estimation. Expressions for the estimation bias and variance of the reverberation decay rate are derived and verified using Monte-Carlo simulations. Comparison is made with a straight-forward linear regression method (not using backward integration). It is shown that, even though significantly reducing the estimation variance, the use of backward integration can in many cases mitigate the estimation accuracy due to large bias. This clearly indicates that prudence is called for when using backward integration for automated decay rate estimation problems.

  • 7.
    Schüldt, Christian
    et al.
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Laptev, Ivan
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Caputo, Barbara
    KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.
    Recognizing human actions: A local SVM approach2004In: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3 / [ed] Kittler, J; Petrou, M; Nixon, M, 2004, p. 32-36Conference paper (Refereed)
    Abstract [en]

    Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.

1 - 7 of 7
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